Amazon AWS Certified Generative AI Developer

Question 6 of 85

A company is building a generative AI (GenAI) application that processes financial reports and provides summaries for analysts. The application must run two compute environments. In one environment, AWS Lambda function must use the Python SDK to analyze reports on demand. In the second environment, Amazon EKS containers must use the JavaScript SDK to batch process multiple reports on a schedule. The application must maintain conversational context throughout multi-tum interactions, use the same foundation model (FM) across environments, and ensure consistent authentication.
Which solution will meet these requirements?

Use the Amazon Bedrock InvokeModel API with a separate authentication method for each environment. Store conversation states in Amazon DynamoDB. Use custom I/O formatting logic for each programming language.

Use the Amazon Bedrock Converse API directly in both environments with a common authentication mechanism that uses IAM roles. Store conversation states in Amazon ElastiCache. Creating programming language-specific wrappers for model parameters.

Create a centralized Amazon API Gateway REST API endpoint that handles all model interactions by using the InvokeModel API. Store interaction history in application process memory in each Lambda function or EKS container. Use environment variables to configure model parameters.

Use the Amazon Bedrock Converse API and IAM roles for authentication. Pass previous messages in the request messages array to maintain conversational context. Use programming language-specific SDKs to establish consistent API interfaces.

Question 7 of 85

A company upgraded its Amazon Bedrock powered foundation model (FM) that supports a multilingual customer service assistant. After the upgrade, the assistant exhibited inconsistent behavior across languages. The assistant began generating different responses in some languages when presented with identical questions.
The company needs a solution to detect and address similar problems for future updates. The evaluation must be completed within 45 minutes for all supported languages. The evaluation must process at least 15,000 test conversations in parallel. The evaluation process must be fully automated and integrated into the CI/CD pipeline. The solution must block deployment if quality thresholds are not met.
Which solution will meet these requirements?

Create a distributed traffic simulation framework that sends translation-heavy workloads to the assistant in multiple languages simultaneously. Use Amazon CloudWatch metrics to monitor latency, concurrency, and throughput. Run simulations before production releases to identify infrastructure bottlenecks.

Deploy the assistant in multiple AWS Regions with Amazon Route 53 latency-based routing and AWS Global Accelerator to improve global performance. Store multilingual conversation logs in Amazon S3. Perform weekly post-deployment audits to review consistency.

Create a pre-processing pipeline that normalizes all incoming messages into a consistent format before sending the messages to the assistant. Apply rule-based checks to flag potential hallucinations in the outputs. Focus the evaluation on the normalized text to simplify testing across languages.

Set up standardized multilingual test conversations with identical meaning. Run the test conversations in parallel by using Amazon Bedrock model evaluation jobs. Apply similarity and hallucination thresholds. Integrate the process into the CI/CD pipeline to block releases that fail.

Question 8 of 85

A company wants to create an annual rewards program for its customers. The rewards that customers earn vary based on different parameters such as the categories of the items ordered and the customers' purchase history.
The company needs a generative AI (GenAI) solution that uses three Amazon Bedrock agents to help customers during online catalog browsing. The agents must use knowledge bases and action groups to handle the search, recommendation, and order modules. The modules must operate sequentially. An AWS Lambda function must calculate estimated rewards for each recommended item. The solution must provide graceful degradation during service disruptions.
Which solution will meet these requirements with the MOST operational efficiency?

Define an Amazon API Gateway REST API behind each agent. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Configure the second Lambda function with a retry/fallback mechanism.

Create an AWS Step Functions state machine with four tasks that run the agents and the rewards Lambda function. Set up retry and catch branches for each of the task steps.

Configure each agent with a separate retry/fallback mechanism. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Define an Amazon API Gateway REST API behind the second Lambda function.

Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Create an AWS Step Functions state machine with one task that runs the second Lambda function. Set up retry and catch branches for the task step.

Question 9 of 85

A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?

Integrate an Amazon API Gateway HTTP API with an AWS Lambda function to invoke Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic for model timeouts by using Lambda and API Gateway timeout configurations.

Connect an Amazon API Gateway HTTP API directly to Amazon Bedrock. Simulate streaming by using client-side polling. Enforce token limits on the frontend. Configure retry behavior by using API Gateway integration settings.

Connect an Amazon API Gateway WebSocket API to an Amazon ECS service that hosts a containerized inference server. Stream responses by using the WebSocket protocol. Enforce token limits within Amazon ECS. Handle model timeouts by using ECS task lifecycle hooks and restart policies.

Integrate an Amazon API Gateway REST API with an AWS Lambda function that invokes Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic by using Lambda and API Gateway timeout configurations.

Question 10 of 85

A retail company is using Amazon Bedrock to develop a customer service AI assistant. Analysis shows that 70% of customer inquiries are simple product questions that a smaller model can effectively handle. However, 30% of inquiries are complex return policy questions that require advanced reasoning. The company wants to implement a cost-effective model selection framework to automatically route customer inquiries to appropriate models based on inquiry complexity. The framework must maintain high customer satisfaction and minimize response latency.
Which solution will meet these requirements with the LEAST implementation effort?

Create a multi-stage architecture that uses a small foundation model (FM) to classify the complexity of each inquiry. Route simple inquiries to a smaller, more cost-effective model. Route complex inquiries to a larger, more capable model. Use AWS Lambda functions to handle the routing logic.

Use Amazon Bedrock intelligent prompt routing to automatically analyze inquiries. Route simple product inquiries to smaller models, and route complex return policy inquiries to more capable larger models.

Implement a single-model solution that uses an Amazon Bedrock mid-sized foundation model (FM) with on-demand pricing. Include special instructions in model prompts to handle both simple and complex inquiries by using the same model.

Create separate Amazon Bedrock endpoints for simple and complex inquiries. Implement a rule-based routing system based on keyword detection. Use on-demand pricing for the smaller model and provisioned throughput for the larger model.